# Mable fit of one-sample grouped data by an optimal or a preselected model degree Maximum approximate Bernstein/Beta likelihood estimation based on one-sample grouped data with an optimal selected by the change-point method among `m0:m1` or a preselected model degree `m`. ```r mable.group( x, breaks, M, interval = c(0, 1), IC = c("none", "aic", "hqic", "all"), vb = 0, controls = mable.ctrl(), progress = TRUE ) ``` ## Arguments - `x`: vector of frequencies - `breaks`: class interval end points - `M`: a positive integer or a vector `(m0, m1)`. If `M = m` or `m0 = m1 = m`, then `m` is a preselected degree. If `m03`. - `interval`: a vector containing the endpoints of support/truncation interval - `IC`: information criterion(s) in addition to Bayesian information criterion (BIC). Current choices are "aic" (Akaike information criterion) and/or "qhic" (Hannan–Quinn information criterion). - `vb`: code for vanishing boundary constraints, -1: f0(a)=0 only, 1: f0(b)=0 only, 2: both, 0: none (default). - `controls`: Object of class `mable.ctrl()` specifying iteration limit and the convergence criterion `eps`. Default is `mable.ctrl`. See Details. - `progress`: if TRUE a text progressbar is displayed ## Returns A list with components * `m` the given or a selected degree by method of change-point * `p` the estimated `p` with degree `m` * `mloglik` the maximum log-likelihood at degree `m` * `interval` supporting interval `(a, b)` * `convergence` An integer code. 0 indicates successful completion (all the EM iterations are convergent and an optimal degree is successfully selected in `M`). Possible error codes are * 1, indicates that the iteration limit `maxit` had been reached in at least one EM iteration; * 2, the search did not finish before `m1`. * `delta` the convergence criterion `delta` value and, if `m0

mable.group function